Data Science Career Paths & Skillsets in 2021

A glimpse of Kaggle’s State of Data Science and Machine Learning Survey

Jingsong Gao, jg2109@georgetown.edu

Ercong Luo, el890@georgetown.edu

Rui Qiu, rq47@georgetown.edu

2021-11-30

Section 1: Introduction

Address the data science problem

Section 2: Data Cleaning

col_names <- names(read_csv(
  "data/kaggle_survey_2021_responses.csv",
  n_max=0))
dat <- read_csv(
  "data/kaggle_survey_2021_responses.csv",
  col_names = col_names, skip=2)

dat <- dat %>%
  filter(Q3=="United States of America" )

job.dat <- dat %>% 
    filter(Q5 %in% c("Data Analyst",
                     "Data Engineer",
                     "Data Scientist",
                     "Machine Learning Engineer",
                     "Software Engineer",
                     "Statistician",
                     "Student"))

Section 3: The Analysis and Results

Since the one large data science problem can be divided into several smaller bits, we decide to combine the analysis and the interpretation sections together. And the questions will be addressed and discussed one by one.

The following questions are about data science career paths.

Q1: Job Titles Distributions

What percentage of the survey respondents are working under these job titles?

jtitle <- sort(table(dat$Q5), decreasing = T) %>% 
    as.data.frame() %>%
    as.tibble()
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
jtitle <- rename(jtitle, `Job Title` = Var1)

ggplot(jtitle, aes(x="", y=Freq, fill=`Job Title`)) +
    geom_bar(stat="identity", width=1, color="white") +
    coord_polar("y", start=0) + 
    theme_void()

Q2: Salaries

What are the statistics on salaries for these job titles?

The US minimum wage is 7.25 per hour, multiply that for a total \(260\) typical number of workdays a year:

\[7.25 \frac{\$}{\text{hour}} \cdot 24 \frac{\text{hours}}{\text{day}} \cdot 260\frac{\text{days}}{\text{year}} = 45240.\]

The new categories* for salaries will be: - poverty: below the federal minimum wage - low: 40,000 to 79,999 - medium: 80,000 to 124,999 - high: 125,000 to 199,999 - very high: 200,000 to 499,999 - highest: >= 500,000

*loosely based on US federal income tax brackets.

# To change salary categories into FACTOR dtype with descending labels
poverty   <- c("$0-999", "1,000-1,999" , "2,000-2,999", "3,000-3,999", "4,000-4,999", "5,000-7,499", "7,500-9,999",
             "10,000-14,999", "15,000-19,999", "20,000-24,999", "25,000-29,999", "30,000-39,999") 
low       <- c("40,000-49,999", "50,000-59,999", "60,000-69,999", "70,000-79,999")
medium    <- c("80,000-89,999", "90,000-99,999", "100,000-124,999")
high      <- c("125,000-149,999", "150,000-199,999")
very_high <- c("200,000-249,999", "250,000-299,999", "300,000-499,999")
highest   <- c("$500,000-999,999", ">$1,000,000")

dat$Q25[dat$Q25 %in% poverty] <- "poverty"
dat$Q25[dat$Q25 %in% low] <- "low"
dat$Q25[dat$Q25 %in% medium] <- "medium"
dat$Q25[dat$Q25 %in% high] <- "high"
dat$Q25[dat$Q25 %in% very_high] <- "very high"
dat$Q25[dat$Q25 %in% highest] <- "highest"
dat$Q25 <- factor(dat$Q25, levels = c("poverty", "low", "medium", "high", "very high", "highest"), ordered = T)

data_side <- c("Data Scientist", "Data Analyst", "Business Analyst", "Data Engineer", "Statistician", "DBA/Database Engineer")
swe_side <- c("Software Engineer", "Machine Learning Engineer", "Program/Project Manager", "Product Manager") 
academia <- c("Student", "Other", "Research Scientist") 

dat[dat$Q5 %in% data_side & !is.na(dat$Q5) & !is.na(dat$Q25), ] %>%
  ggplot( aes(x=Q5, y=Q25, color = Q5)) +
    geom_count() + 
    ggtitle("Two-Way Salary Visualizations: Data-Oriented Jobs") +
    xlab("") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
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dat[dat$Q5 %in% swe_side & !is.na(dat$Q5) & !is.na(dat$Q25), ] %>%
  ggplot( aes(x=Q5, y=Q25, color = Q5)) +
    geom_count() + 
    ggtitle("Two-Way Salary Visualizations: Engineering-Oriented Jobs") +
    xlab("") + 
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
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dat[dat$Q5 %in% academia & !is.na(dat$Q5) & !is.na(dat$Q25), ] %>%
  ggplot( aes(x=Q5, y=Q25, color = Q5)) +
    geom_count() + 
    ggtitle("Two-Way Salary Visualizations: Academic Jobs and Others") +
    xlab("") + 
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
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Q3: Requried Education Levels

What levels of education are required for these job titles?

Q4: Gender Gap in Income

Is there a significant income gap between genders for these jobs?

Q5: Skillset vs Income

What is the typical skill set for these jobs? How does it affect the pay rate?

skill.set <- job.dat %>% 
    select(c(Q5, starts_with("Q7_"), starts_with("Q9_"), 
             starts_with("Q12_"), starts_with("Q14_"),
             starts_with("Q16_"), starts_with("Q17_"),
             starts_with("Q18_"), starts_with("Q19_"))) %>%
    mutate(Total = "TotalHelper") %>%
    gather("fake_key", "skillset", -Q5, na.rm = T) %>%
    filter(!skillset %in% c("None", "Other")) %>%
    rename(title = Q5) %>%
    count(title, skillset) %>%
    group_by(title) %>%
    mutate(prop = round(n / max(n), 3)) %>%
    filter(prop >= 0.1 & skillset != "TotalHelper") %>%
    select(-n) %>%
    arrange(title, desc(prop))

datatable(skill.set, filter = 'top', width = 600)

Q6: Correlation between Industry and Job

Is there a certain correlation between industry and the need for these jobs?

industry.dat <- job.dat %>%
    filter(Q5 != "Student") %>%
    select(Q5, Q20, Q25) %>%
    filter(Q20 %in% c("Academics/Education", 
                      "Accounting/Finance", 
                      "Computers/Technology",
                      "Insurance/Risk Assessment",
                      "Medical/Pharmaceutical",
                      "Online Service/Internet-based Services")) %>%
    mutate(Q25 = str_remove_all(Q25, "[$,]")) %>%
    mutate(Q25 = str_replace(Q25, ">1000000", "1000000-2000000")) %>%
    separate(Q25, into = c("salary_lb", "salary_ub"), sep = "-") %>%
    mutate(salary_lb = as.numeric(salary_lb)) %>%
    mutate(salary_ub = as.numeric(salary_ub))

p <- industry.dat %>% 
    count(Q5, Q20) %>%
    mutate(Q20 = fct_reorder(Q20, n, .fun="sum")) %>%
    rename(title=Q5, Industry=Q20, count=n) %>%
    ggplot(aes(x=title, y=count)) +
    geom_bar(stat = "identity") +
    coord_flip() +
    facet_wrap(~ Industry) +
    labs(
        title = "Users' work industry",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
ggplotly(p)
chisq.test(table(industry.dat$Q5, industry.dat$Q20))
## Warning in chisq.test(table(industry.dat$Q5, industry.dat$Q20)): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  table(industry.dat$Q5, industry.dat$Q20)
## X-squared = 108.6, df = 25, p-value = 2.153e-12
industry.dat %>% 
    mutate(Q20 = fct_reorder(Q20, salary_lb, .fun='length')) %>%
    ggplot(aes(x=Q20, y=salary_lb)) +
    geom_boxplot() +
    coord_flip() +
    facet_wrap(~ Q5) +
    labs(
        title = "Users' salary vs industry",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
## Warning: Removed 35 rows containing non-finite values (stat_boxplot).
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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列
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## Windows字体数据库里没有这样的字体系列
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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列
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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

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## Windows字体数据库里没有这样的字体系列

The following questions are majorly about data science skills, tools and technologies.

Q7: Languages and IDEs

What programming languages and IDEs do they use?

Survey questions Q7 (daily-used programming language), Q9 (IDE).

programming <- job.dat %>% 
    select(c(Q5, starts_with("Q7_"))) %>%
    gather("fake_key", "language", -Q5, na.rm = T) %>%
    rename(title = Q5) %>%
    select(-fake_key) %>%
    filter(!language %in% c("None", "Other")) %>%
    count(title, language, .drop = FALSE) %>% 
    complete(title, language) %>%
    replace_na(list(n = 0)) %>%
    group_by(title) %>%
    mutate(prop = prop.table(n))

p <- programming %>% 
    mutate(text = paste0("Language: ", language, "\n", 
                         "Job title: ", title, "\n", 
                         "Count: ", n, "\n",
                         "Proportion: ", round(prop, 3))) %>%
    ggplot(aes(language, title, fill=prop, text=text)) +
    geom_tile() +
    scale_fill_gradient(low="white", high="blue") +
    labs(
        title = "Users' favorite programming language",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
ggplotly(p, tooltip="text")
## Warning: plotly.js does not (yet) support horizontal legend items 
## You can track progress here: 
## https://github.com/plotly/plotly.js/issues/53
ide <- job.dat %>% 
    select(c(Q5, starts_with("Q9_"))) %>%
    gather("fake_key", "IDE", -Q5, na.rm = T) %>%
    rename(title = Q5) %>%
    select(-fake_key) %>%
    mutate(IDE = case_when(
        IDE == "Visual Studio Code (VSCode)" ~ "VSCode",
        IDE == "Jupyter (JupyterLab, Jupyter Notebooks, etc)" ~ "Jupyter Notebook",
        TRUE ~ IDE
    )) %>%
    filter(!IDE %in% c("None", "Other")) %>%
    count(title, IDE, .drop = FALSE) %>% 
    complete(title, IDE) %>%
    replace_na(list(n = 0)) %>%
    group_by(title) %>%
    mutate(prop = prop.table(n))

p <- ide %>% 
    mutate(text = paste0("IDE: ", IDE, "\n", 
                         "Job title: ", title, "\n", 
                         "Count: ", n, "\n",
                         "Proportion: ", round(prop, 3))) %>%
    ggplot(aes(IDE, title, fill=prop, text=text)) +
    geom_tile() +
    scale_fill_gradient(low="white", high="blue") +
    labs(
        title = "Users' favorite IDE",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
ggplotly(p, tooltip="text")
## Warning: plotly.js does not (yet) support horizontal legend items 
## You can track progress here: 
## https://github.com/plotly/plotly.js/issues/53

Q8: Learning Sources

Where do they get and share the knowledge?

Survey questions Q39 (share and deploy), Q40 (learning resources), Q42 (Media sources).

learning_platform <- job.dat %>%
    select(c(Q5, starts_with("Q40_"))) %>%
    gather("fake_key", "learning", -Q5, na.rm = T) %>%
    rename(title = Q5) %>%
    select(-fake_key) %>%
    mutate(learning = case_when(
        learning == "Cloud-certification programs (direct from AWS, Azure, GCP, or similar)" ~ "Cloud-certif Programs",
        learning == "University Courses (resulting in a university degree)" ~ "University",
        TRUE ~ learning
    )) %>%
    filter(!learning %in% c("None", "Other")) %>%
    count(title, learning, .drop = FALSE) %>%
    complete(title, learning) %>%
    replace_na(list(n = 0)) %>%
    group_by(title) %>%
    mutate(prop = prop.table(n))

p <- learning_platform %>%
    mutate(text = paste0("Platform: ", learning, "\n",
                         "Job title: ", title, "\n",
                         "Count: ", n, "\n",
                         "Proportion: ", round(prop, 3))) %>%
    ggplot(aes(learning, title, fill=prop, text=text)) +
    geom_tile() +
    scale_fill_gradient(low="white", high="blue") +
    labs(
        title = "Users' favorite learning platforms",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
ggplotly(p, tooltip="text")
## Warning: plotly.js does not (yet) support horizontal legend items 
## You can track progress here: 
## https://github.com/plotly/plotly.js/issues/53
share_deploy <- job.dat %>% 
    select(c(Q5, starts_with("Q39_"))) %>%
    gather("fake_key", "share", -Q5, na.rm = T) %>%
    rename(title = Q5) %>%
    select(-fake_key) %>%
    mutate(share = case_when(
        share == "I do not share my work publicly" ~ "\'PRIVATE\'",
        TRUE ~ share
    )) %>%
    filter(!share %in% c("Other")) %>%
    count(title, share, .drop = FALSE) %>% 
    complete(title, share) %>%
    replace_na(list(n = 0)) %>%
    group_by(title) %>%
    mutate(prop = prop.table(n))

p <- share_deploy %>% 
    mutate(text = paste0("Platform: ", share, "\n", 
                         "Job title: ", title, "\n",
                         "Count: ", n, "\n",
                         "Proportion: ", round(prop, 3))) %>%
    ggplot(aes(share, title, fill=prop, text=text)) +
    geom_tile() +
    scale_fill_gradient(low="white", high="blue") +
    labs(
        title = "Users' favorite share platforms",
        x = "",
        y = "",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
ggplotly(p, tooltip="text")
## Warning: plotly.js does not (yet) support horizontal legend items 
## You can track progress here: 
## https://github.com/plotly/plotly.js/issues/53
media_source <- job.dat %>% 
    select(c(Q5, starts_with("Q42_"))) %>%
    gather("fake_key", "media", -Q5, na.rm = T) %>%
    rename(title = Q5) %>%
    select(-fake_key) %>%
    filter(!media %in% c("None", "Other")) %>%
    count(title, media, .drop = FALSE) %>% 
    complete(title, media) %>%
    replace_na(list(n = 0)) %>%
    group_by(title) %>%
    mutate(prop = prop.table(n)) %>%
    separate(media, into = c("media", "media_suffix"), sep = " \\(")

p <- media_source %>% 
    mutate(text = paste0("Platform: ", media, "\n", 
                         "Job title: ", title, "\n", 
                         "Count: ", n, "\n", 
                         "Proportion: ", round(prop, 3))) %>%
    ggplot(aes(media, title, fill=prop, text=text)) +
    geom_tile() +
    scale_fill_gradient(low="white", high="blue") +
    labs(
        title = "Users' favorite media source",
        caption = glue("Author: celeritasML
                   Source: Kaggle")) +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=90, hjust=1),
          axis.title = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())
ggplotly(p, tooltip="text")
## Warning: plotly.js does not (yet) support horizontal legend items 
## You can track progress here: 
## https://github.com/plotly/plotly.js/issues/53

Q10: Cloud Computing Platforms Preferences

Will a user’s preference for cloud computing platforms affect his or her preference for other tools? For example, we want to know if an AWS EC2 dedicated user will actually prefer AWS S3 over other products.

Survey question Q29-A: computing products (Part_1) Survey question Q30: Storage (Part_3, Part_4) Survey question Q31-A: ML products (Part_1)

\[\chi^2=\sum\frac{(O_i-E_i)^2}{E_i}\]

aws_user <- tibble(
  ec2 = dat$Q29_A_Part_1,
  s3 = dat$Q30_A_Part_3,
  efs = dat$Q30_A_Part_4,
  sagemaker = dat$Q31_A_Part_1,
  redshift = dat$Q32_A_Part_11,
  aurora = dat$Q32_A_Part_12,
  rds = dat$Q32_A_Part_13,
  dynamodb = dat$Q32_A_Part_14
  
) %>%
  mutate(ec2 = if_else(is.na(ec2), 0, 1),
         s3 = if_else(is.na(s3), 0, 1),
         efs = if_else(is.na(efs), 0, 1),
         sagemaker = if_else(is.na(sagemaker), 0, 1),
         redshift = if_else(is.na(redshift), 0, 1),
         aurora = if_else(is.na(aurora), 0, 1),
         rds = if_else(is.na(rds), 0, 1),
         dynamodb = if_else(is.na(dynamodb), 0, 1))
chisq.test(aws_user$ec2, aws_user$s3)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  aws_user$ec2 and aws_user$s3
## X-squared = 1532, df = 1, p-value < 2.2e-16
chisq.test(aws_user$ec2, aws_user$efs)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  aws_user$ec2 and aws_user$efs
## X-squared = 637.99, df = 1, p-value < 2.2e-16
chisq.test(aws_user$ec2, aws_user$sagemaker)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  aws_user$ec2 and aws_user$sagemaker
## X-squared = 566.34, df = 1, p-value < 2.2e-16

Q11: AWS vs GCP

What is the overall AWS usage percentage among DS practitioners? Is it the same for Google Cloud?

\(H_0: p_A=p_B\), \(H_a:p_A\not=p_B\), where \(A\) and \(B\) can be replaced by AWS, Azure, or GCP, and \(n_A\), \(n_B\) are sample size of group \(A\) and \(B\) respectively.

The test statistic (z-statistic) can be calculated as follow:

\[z=\frac{p_A-p_B}{\sqrt{p(1-p)/n_A+p(1-p)/n_B}}\]

cloud_comp <- tibble(
  aws_usage = dat$Q27_A_Part_1,
  azure_usage = dat$Q27_A_Part_2,
  gcp_usage = dat$Q27_A_Part_3
) %>%
  mutate(aws_usage = if_else(is.na(aws_usage), FALSE, TRUE),
         azure_usage = if_else(is.na(azure_usage), FALSE, TRUE),
         gcp_usage = if_else(is.na(gcp_usage), FALSE, TRUE))
prop.test(c(sum(cloud_comp$aws_usage), sum(cloud_comp$azure_usage)),
          c(nrow(cloud_comp), nrow(cloud_comp)),
          alternative = "two.sided",
          correct = TRUE)
## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(sum(cloud_comp$aws_usage), sum(cloud_comp$azure_usage)) out of c(nrow(cloud_comp), nrow(cloud_comp))
## X-squared = 81.285, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  0.07606175 0.11865523
## sample estimates:
##    prop 1    prop 2 
## 0.2377358 0.1403774
prop.test(c(sum(cloud_comp$azure_usage), sum(cloud_comp$gcp_usage)),
          c(nrow(cloud_comp), nrow(cloud_comp)),
          alternative = "two.sided",
          correct = TRUE)
## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(sum(cloud_comp$azure_usage), sum(cloud_comp$gcp_usage)) out of c(nrow(cloud_comp), nrow(cloud_comp))
## X-squared = 1.8823, df = 1, p-value = 0.1701
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.005495458  0.031910552
## sample estimates:
##    prop 1    prop 2 
## 0.1403774 0.1271698
prop.test(c(sum(cloud_comp$gcp_usage), sum(cloud_comp$aws_usage)),
          c(nrow(cloud_comp), nrow(cloud_comp)),
          alternative = "two.sided",
          correct = TRUE)
## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(sum(cloud_comp$gcp_usage), sum(cloud_comp$aws_usage)) out of c(nrow(cloud_comp), nrow(cloud_comp))
## X-squared = 107.85, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.1315249 -0.0896072
## sample estimates:
##    prop 1    prop 2 
## 0.1271698 0.2377358

Section 4: Conclusions

Appendix: R scripts

# R scripts here.

See footnote 11 See GitHub repository tufte..

Since the same codings will be used in Problem 1-c, I will just use this part as a setup.